Fast Stochastic Subspace Identification of Densely Instrumented Bridges Using Randomized SVD

TL;DR

This paper presents a fast, randomized SVD-based method for efficient operational modal analysis of densely instrumented bridges, improving computational speed and reliability in structural health monitoring.

Contribution

It introduces the use of randomized SVD within stochastic subspace identification to handle large-scale bridge data more efficiently than traditional methods.

Findings

Successful application to San Faustino Bridge with over 60 accelerometers

Enhanced modal identification accuracy and stability

Reduced computational effort compared to classical SVD methods

Abstract

The rising number of bridge collapses worldwide has compelled governments to introduce predictive maintenance strategies to extend structural lifespan. In this context, vibration-based Structural Health Monitoring (SHM) techniques utilizing Operational Modal Analysis (OMA) are favored for their non-destructive and global assessment capabilities. However, long multi-span bridges instrumented with dense arrays of accelerometers present a particular challenge, as the computational demands of classical OMA techniques in such cases are incompatible with long-term SHM. To address this issue, this paper introduces Randomized Singular Value Decomposition (RSVD) as an efficient alternative to traditional SVD within Covariance-driven Stochastic Subspace Identification (CoV-SSI). The efficacy of RSVD is also leveraged to enhance modal identification results and reduce the need for expert intervention by means of 3D stabilization diagrams, which facilitate the investigation of the modal estimates over different model orders and time lags. The approach's effectiveness is demonstrated on the San Faustino Bridge in Italy, equipped with over 60 multiaxial accelerometers.